Building BRIDGEs: Coordinating Standards, Diversity, and Ethics to Advance Biomedical AI

搭建桥梁：协调标准、多样性和道德以推进生物医学人工智能

• 批准号: 10801686
• 负责人: ALEX BUI
• 金额: $ 58.6万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-06 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10801686
• 关键词: Adoption; Anxiety; Appointment; Automobile Driving; Award; Bridge to Artificial Intelligence; Clinical; Communities; Complement component C1; Consultations; Data Science; E-learning; Education; Educational Curriculum; Equity; Ethics; Faculty; Fostering; Future; Goals; Growth; Healthcare; Hour; Leadership; Medical; Medical Informatics; Medical Students; Medicine; Mentors; Mentorship; Mission; Modernization; Nurses; Physicians; Process; Protocols documentation; Reporting; Research Personnel; Societies; Specialist; Strategic Planning; Technology; Training; Trust; Underrepresented Minority; United States National Institutes of Health; Vision; career; clinical care; clinical practice; cohort; community building; design; diverse data; driving force; empowerment; experience; innovation; medical schools; minority communities; multidisciplinary; next generation; programs; recruit; skills; trustworthiness

The NIH's Strategic Plan for Data Science sets forth a grand and ambitious goal to enhance the diversity of the data science workforce and to engage underrepresented minority communities. Indeed, the vision of an ethical and equitable society supported by modern AI/ML healthcare innovations can only be achieved when our stake- holders include representatives of all communities. In lockstep with the NIH's diversity goals, this application aims to enact long-lasting change in the biomedical AI/ML community by a key leadership workforce to advance our mission in fostering the growth of a diverse next-generation multidisciplinary cohort of physicians and inves- tigators who are the driving force enabling Bridge2AI. Briefly, we recognize a major challenge in biomedical AI/ML education that limits the widespread adoption of modern AI/ML in health care and biomedical innovations. There exists a general lack of understanding regarding ethical and trustworthy AI (ETAI) in our workforce, com- bined with public anxiety surrounding the use of AI/ML applications. These concerns stifle the potential impact of these AI/ML technologies. Accordingly, we propose to establish the Bridge2AI-ENABLE Scholar Award. Essential to the successful implementation of AI/ML strategies are the emerging underrepresented in medicine (URiM); these professionals (e.g., medical students, clinical fellows, nurses, physicians) are well-trained in their medical professions and are committed to undergo further training in AI/ML and to advance clinical practice. Our proposed Bridge2AI-ENABLE Scholar Award will offer targeted AI/ML training for up to 15 URiM medical profes- sionals who are poised to become future leaders driving AI/ML innovation in health care. This URiM enrichment activity will consist of designated mentor teams composed of clinicians, AI/ML specialists, and ETAI leaders. We have organized a comprehensive 10-week long training plan that includes: 1) a well-thought-out applicant re- cruitment plan and mentee appointment protocol; 2) a mentor team with strong commitment from UCLA School of Medicine, medical informatics, and computational medicine faculty that follow our mentor selection processes; and 3) a customized curriculum tailoring each mentee to completing their training. Overall, our training platform aims to overcome anxiety surrounding biomedical AI/ML, build community trust, and empower trainees exploring biomedical AI/ML topics with entry at ground zero. Each trainee can design their personalized curriculum and complete AI education at their own pace. The majority of the curriculum is conducted via an e-learning platform, complemented by 1-on-1 mentorship and A&Q consultation hours. Each mentee will partner with the SWD Core to customize training plans and design their personalized curriculum, which enables them to complete AI edu- cation in their own space and to carry out AI/ML projects in real-world scenarios. The mentor teams will be responsible for creating milestone reports for each mentee to steer a successful career trajectory. The goal of this supplemental activity is to support URiM medical professionals to acquire necessary understanding and skills in AI/ML and to enable them to be the driving force to advance AI strategies in modern healthcare.

美国国立卫生研究院的数据科学战略计划提出了一个宏伟而雄心勃勃的目标，以增强数据科学的多样性 数据科学劳动力并让代表性不足的少数群体参与其中。事实上，道德的愿景 只有当我们的利益相关者 持有人包括所有社区的代表。该应用程序与 NIH 的多样性目标保持一致 旨在通过关键领导团队在生物医学 AI/ML 社区中实现持久的变革，以推进 我们的使命是促进多元化的下一代多学科医生和研究人员的成长 老虎们是 Bridge2AI 的驱动力。简而言之，我们认识到生物医学领域面临的重大挑战 人工智能/机器学习教育限制了现代人工智能/机器学习在医疗保健和生物医学创新领域的广泛采用。 我们的员工、公司普遍缺乏对道德和值得信赖的人工智能 (ETAI) 的理解。 加上公众对使用人工智能/机器学习应用程序的焦虑。这些担忧抑制了潜在的影响 这些人工智能/机器学习技术。因此，我们建议设立 Bridge2AI-ENABLE 学者奖。 成功实施人工智能/机器学习战略的关键是医学领域中出现的代表性不足的问题 （乌里姆）；这些专业人员（例如医学生、临床研究员、护士、医生）在其专业领域受过良好培训 医学专业人士，并致力于接受人工智能/机器学习方面的进一步培训并推进临床实践。我们的 拟议的 Bridge2AI-ENABLE 学者奖将为最多 15 名 URiM 医疗专业人员提供有针对性的 AI/ML 培训 那些有望成为推动医疗保健领域人工智能/机器学习创新的未来领导者的人。这个 URiM 丰富 活动将由指定的导师团队组成，该团队由临床医生、AI/ML 专家和 ETAI 领导者组成。我们 组织了为期 10 周的全面培训计划，其中包括：1）经过深思熟虑的申请人重新 招聘计划和学员任命协议； 2) 来自加州大学洛杉矶分校学院坚定承诺的导师团队 遵循我们导师选择流程的医学、医学信息学和计算医学教师； 3) 定制课程，为每个学员量身定制以完成培训。总体而言，我们的培训平台 旨在克服围绕生物医学人工智能/机器学习的焦虑，建立社区信任，并赋予学员探索的能力 从零开始入门的生物医学 AI/ML 主题。每个学员都可以设计适合自己的个性化课程 按照自己的节奏完成人工智能教育。大部分课程是通过电子学习平台进行的， 辅以一对一指导和 A&Q 咨询时间。每个受训者都将与 SWD Core 合作 定制培训计划，设计个性化课程，使他们能够完成AI教育 在自己的空间中进行阳离子处理，并在现实场景中开展人工智能/机器学习项目。导师团队将是 负责为每个学员创建里程碑报告，以引导成功的职业轨迹。目标是 这项补充活动旨在支持 URiM 医疗专业人员获得必要的理解和 人工智能/机器学习技能，并使他们成为推进现代医疗保健人工智能战略的驱动力。